Improved evolutionary programming with dynamic mutation and metropolis criteria for multi-objective reactive power optimisation

Abstract

Reactive power optimisation is an important technique, which is concerned with the security and economy of operation of the power system. The appropriate distribution of reactive power can elevate voltage rating, decrease network losses and maintain network running under proper conditions. An improved evolutionary programming method with dynamic mutation and metropolis selection to solve the multi-objective reactive power optimisation under the deregulation environment is presented. The multi-objective function includes the minimisation of network losses, voltage deviation and compensation cost. To solve the problems of convergence and robustness in the conventional evolutionary programming method, the mutation operators and the selection criteria that affect the convergence and robustness are considered and a dynamic mutation and metropolis selection evolutionary programming method is suggested. Introducing chaos dynamics into mutation operators of evolutionary programming, the new method adopts the certainty method of like-stochastic to obtain mutation operators which break through the conventional thought with mutation by stochastic numbers of fixed distribution. Also, it introduces the metropolis selection in evolutionary programming to construct new selection operators. Thus, the new method not only accelerates convergence but also increases precision, so is an efficient way to optimise the capacitor banks and the adjustable transformer ratio. Tested by the IEEE-30 bus system, the method is effective.